Degradation Modeling
Inverse Gaussian processes, multivariate degradation, two-phase and two-scale reliability models.
Liangliang Zhuang
Seeking knowledge, leading with courage.
Reliability
Statistics
Bayesian
Degradation
Postdoc
PHM
RUL
Optimization
Research
Predictive Maintenance
Remaining useful life prediction, sensor degradation, Bayesian deep learning, and PHM workflows.
Bayesian & AI Systems
Hierarchical Bayesian inference, adaptive sampling, federated learning, and data-driven optimization.
Selected papers
Modeling two-scale degradation with heterogeneity: A unified random-effects inverse Gaussian framework
Liangliang Zhuang, Yizhong Ma, Guanqi Fang, and Ancha Xu*. IISE Transactions, 2026.
From Statistical Modeling to AI-Integrated Inverse Gaussian Process: A Comprehensive Review for Prognostics and Health Management
Liangliang Zhuang, Yizhong Ma, Jianjun Wang, Rong Pan, and Ancha Xu*. Engineering Management, 2026, 13(1): 65.
Multivariate reparameterized inverse Gaussian processes with common effects for degradation-based reliability prediction
Liangliang Zhuang, Ancha Xu*, Guanqi Fang, and Yincai Tang. Journal of Quality Technology, 57(1): 51-67.
Recent updates
Presented at the 2026 Annual Conference of the Reliability Sub-Society of ORSC in Beijing Forestry University.
Presented an online seminar at Reliability Home on reparameterized inverse Gaussian processes.
The two-scale degradation paper was accepted by IISE Transactions.
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